Once the imprint is obtained for each video, it makes reasoning based on this compact imprint representation possible, where each location corresponds to a recurring scene/object part, with spatial layouts of imprint locations reflecting the scene/object spatial layouts in video frames where they were observed. We treat locations in the imprint in a similar way to the sentences in memory networks [1]}, [2]}. Our reasoning network determines the event category in stages, which traverses attention from one set of imprint locations to the next. In this process as illustrated in Figure REF , the imprint locations of large importance are highlighted, and we also trace these highlights back to the locations in video frames using the \(q/\hat{q}\)  distributions as discussed above.
